Machine Learning And AI Opportunities

Note: This is a Pipeline Opening and Not Tied to A Specific Opening

The AI Product Manager owns strategy, roadmap, and delivery for AI initiatives across the enterprise. This role translates business objectives into a clear prioritized backlog, collaborating with engineering teams and external partners to deliver impactful solutions that drive measurable business impact through revenue growth or new operational efficiencies. The Product Manager works closely with Global Data Product Management, including Data and MDM, to ensure AI products are built on trusted, standardized data foundations. The role requires strong judgment in application integrations and build vs. buy tradeoffs, balancing speed, cost, and enterprise standards.

Key Responsibilities

Area

What You'll Own

Product Strategy & Vision

Define a 3-9-month AI roadmap aligned to company strategic goals, with clear problem statements and value hypotheses to maximize value.

Portfolio & Roadmap

Convert strategy into prioritized increments with PRDs, acceptance criteria, and release plans; keep a transparent backlog.

Evaluation & Experimentation

Own evaluation for AI features: offline tests, human and automated evals, and online A/B experiments to optimize quality, latency, and cost effectiveness. Publish decision logs for model, prompt, and dataset changes.

Responsible AI & Guardrails

Define safety, privacy, and compliance requirements; implement guardrails and review rituals with Security and Legal; ensure traceability of data, prompts, and outputs.

Application Integrations

Define product requirements for API-first and event driven integrations across CRM/ERP/eCommerce and data platforms; align on data contracts, SLAs, auth/PII handling, and system observability with Platform teams.

Build vs Buy

Lead structured tradeoff analyses (TTV, TCO, vendor lock in, differentiation, compliance). Run proofs of value with vendors when needed and recommend paths forward, highlighting risks and contingency plans..

Cross‑Functional Leadership

Lead squads spanning ML Engineering, MLOps, Data Engineering, Analytics, and Business stakeholders; keep scope, risks, and dependencies visible.

Impact Measurement

Define clear KPIs that connect business outcomes to product performance, and provide executives with simple, actionable reporting against those targets.

Partnerships

Collaborate with Director, ML and AI, Security, Legal, Procurement, and Global Data Product Management (Data and MDM) to align standards, governance, and delivery.

Required Qualifications

• 5+ years in product management with shipped data or AI features tied to measurable outcomes.
• Proven ownership of evaluation and experimentation for AI features (offline metrics, human/auto evals, and A/B testing).
• Handson experiences driving application integrations at enterprise scale (APIled and iPaaS patterns, SLAs, data contracts, identity).
• Demonstrated ability to lead build vs. buy decisions, supported by clear financial models and risk analyses.
• Excellent executive communication and stakeholder leadership.

Preferred Qualifications

• Experience optimizing Amazon sales and ad channels.
• Experience with Salesforce Cloud and CDP integrations.
• Exposure to LLMOps practices (prompt versioning, guardrails, eval frameworks), vector search/RAG, or model observability.

Success Metrics (first 12 months)

• AI roadmap approved and in execution within 90 days.At least two AI product increments shipped with adoption targets met.
• Application integrations delivered on time with measurable data quality and reliability improvements.
• At least two major technology decisions completed with a formal build vs. Buy analysis and exec approval.
• AI initiatives deliver a minimum 5x ROI on Capex investments, contributing directly to revenue growth.